This Phase II SBIR is prompted by the need for more effective Augmentative and Alternative Communication (AAC) devices for persons unable to communicate through vocalization. The project follows our preliminary work, which convincingly demonstrated that surface electromyographic (sEMG) signals recorded from speech articulation muscles can provide a new and effective form of communication without vocalization. Because sEMG-based speech recognition does not rely on acoustic excitation of the vocal tract, it is readily applicable to recognizing subvocal (i.e. mouthed) speech. Subvocal speech is therefore an obvious alternative form of communication for patients with laryngectomy. The goal of this project is to deliver a pre-commercial, wearable, subvocal speech recognition (SSR) system operating on an Android mobile device (Smartphone) that can provide non-speakers with a laryngectomy the ability to produce hands-free, intelligible communication in the home, community, or over the phone. The project is well positioned for direct Phase II development. Proof-of- principal and reduced-risk have been achieved on two fronts: i) wireless sensor designs have been successfully implemented in a rudimentary prototype that improves the task of recording sEMG signals from 8 articulatory muscles of the face and neck; and ii) the most advanced SSR engine to date has been formulated to achieve accurate recognition of subvocal continuous speech from a 2000 word vocabulary tested on unimpaired speakers as well as from 2 people with laryngectomy. Phase II will advance these technologies by reducing the requisite sensor set to just facial muscle sites, which will be integrated into a pre-commercial device for use by non-speakers with a laryngectomy. Aim 1 will consolidate the individual sEMG sensors into a conformable facial interface and combine the acquired signals into a data stream for Bluetooth connectivity to the Android device running the SSR software. The resulting data acquisition system will be encapsulated, bench-tested, and evaluated on subjects with a laryngectomy. Aim 2 will create an advanced SSR engine for laryngectomy users that will reduce the requisite number of sensors from 8, to a sub-set of 4 on the face, while attaining a recognition performance for 1000 words at an error rate less than 10%. The impact of this innovation is that it provides laryngectomy users with an alternative form of speech that a) overcomes the limitations of current automated speech recognition (ASR) systems that are microphone dependent, b) is hands-free compared to electrolarynx technologies requiring handheld contact, c) does not suffer from poor intelligibility or the need for surgical interventio and maintenance as with current voice prostheses, and d) is readily adaptable as a man-machine interface for AAC devices.